We demonstrate how efficient autonomous drone swarms can be in detecting and tracking occluded targets in densely forested areas, such as lost people during search and rescue missions. Exploration and optimization of local viewing conditions, such as occlusion density and target view obliqueness, provide much faster and much more reliable results than previous, blind sampling strategies that are based on pre-defined waypoints. An adapted real-time particle swarm optimization and a new objective function are presented that are able to deal with dynamic and highly random through-foliage conditions. Synthetic aperture sensing is our fundamental sampling principle, and drone swarms are employed to approximate the optical signals of extremely wide and adaptable airborne lenses.

In this article, we evaluate unsupervised anomaly detection methods in multispectral images obtained with a wavelength-independent synthetic aperture sensing technique, called Airborne Optical Sectioning (AOS). With a focus on search and rescue missions that apply drones to locate missing or injured persons in dense forest and require real-time operation, we evaluate runtime vs. quality of these methods. Furthermore, we show that color anomaly detection methods that normally operate in the visual range always benefit from an additional far infrared (thermal) channel. We also show that, even without additional thermal bands, the choice of color space in the visual range already has an impact on the detection results. Color spaces like HSV and HLS have the potential to outperform the widely used RGB color space, especially when color anomaly detection is used for forest-like environments.

我们提出了逆空气流化截面（IAOS）与逆合成孔径雷达（ISAR）的光学类比。可以通过固定的光学传感器（例如，悬停在森林上方的悬停相机无人驾驶飞机）对移动的目标，例如走路人，被植被遮住了。我们介绍了IAOS的原理（即，逆合孔径成像），解释如何通过过滤图像积分的ra radon变换来进一步抑制封闭器的信号，并介绍如何手动和自动估算目标运动参数。最后，我们表明，尽管在常规航空图像中跟踪封闭目标是不可行的，但在IAOS产生的积分图像中，它变得有效。

通过叶子检测和跟踪移动目标是困难的，并且在许多情况下甚至不可能在常规空中图像和视频中。我们提出了一种初始轻型和无人机操作的1D摄像头阵列，其支持并行合成孔径空中成像。我们的主要发现是，与传统的单个图像或视频帧相比，颜色异常检测效益显着从图像集成时（平均97％在我们的现场实验中的精确度）。我们展示，这两项贡献可能导致通过密集封闭森林的检测和跟踪移动人员

When answering natural language questions over knowledge bases (KBs), incompleteness in the KB can naturally lead to many questions being unanswerable. While answerability has been explored in other QA settings, it has not been studied for QA over knowledge bases (KBQA). We first identify various forms of KB incompleteness that can result in a question being unanswerable. We then propose GrailQAbility, a new benchmark dataset, which systematically modifies GrailQA (a popular KBQA dataset) to represent all these incompleteness issues. Testing two state-of-the-art KBQA models (trained on original GrailQA as well as our GrailQAbility), we find that both models struggle to detect unanswerable questions, or sometimes detect them for the wrong reasons. Consequently, both models suffer significant loss in performance, underscoring the need for further research in making KBQA systems robust to unanswerability.

Dengue fever is a virulent disease spreading over 100 tropical and subtropical countries in Africa, the Americas, and Asia. This arboviral disease affects around 400 million people globally, severely distressing the healthcare systems. The unavailability of a specific drug and ready-to-use vaccine makes the situation worse. Hence, policymakers must rely on early warning systems to control intervention-related decisions. Forecasts routinely provide critical information for dangerous epidemic events. However, the available forecasting models (e.g., weather-driven mechanistic, statistical time series, and machine learning models) lack a clear understanding of different components to improve prediction accuracy and often provide unstable and unreliable forecasts. This study proposes an ensemble wavelet neural network with exogenous factor(s) (XEWNet) model that can produce reliable estimates for dengue outbreak prediction for three geographical regions, namely San Juan, Iquitos, and Ahmedabad. The proposed XEWNet model is flexible and can easily incorporate exogenous climate variable(s) confirmed by statistical causality tests in its scalable framework. The proposed model is an integrated approach that uses wavelet transformation into an ensemble neural network framework that helps in generating more reliable long-term forecasts. The proposed XEWNet allows complex non-linear relationships between the dengue incidence cases and rainfall; however, mathematically interpretable, fast in execution, and easily comprehensible. The proposal's competitiveness is measured using computational experiments based on various statistical metrics and several statistical comparison tests. In comparison with statistical, machine learning, and deep learning methods, our proposed XEWNet performs better in 75% of the cases for short-term and long-term forecasting of dengue incidence.